A MOPSO method for Direct Load Control in Smart Grid

Expert Systems with Applications

Volumn 42, Issue 21, 2015, Pages 7456-7465

  Evora, Jose ^a   Hernandez, Jose Juan ^a   Hernandez, Mario ^a  

^a UNIVERSITY OF LAS PALMAS DE GRAN CANARIA   (Spain)

Author keywords

Agent based model; Complex system; Demand Side Management; Optimisation; Particle Swarm Optimisation; Smart Grid

Indexed keywords

AUTONOMOUS AGENTS; COMPUTATIONAL METHODS; ELECTRIC POWER SYSTEM CONTROL; ELECTRIC UTILITIES; LARGE SCALE SYSTEMS; MULTIOBJECTIVE OPTIMIZATION; PARTICLE SWARM OPTIMIZATION (PSO); SMART POWER GRIDS;

AGENT-BASED MODEL; DEMAND SIDE MANAGEMENTS; OPTIMISATIONS; PARTICLE SWARM OPTIMISATION; SMART GRID;

ELECTRIC LOAD MANAGEMENT;

EID: 84934984468     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2015.05.056     Document Type: Article

References (28)

1. M. Alonso, H. Amaris, and C. Alvarez-Ortega Integration of renewable energy sources in smart grids by means of evolutionary optimization algorithms Expert Systems with Applications 39 2012 5513 5522 10.1016/j.eswa.2011.11.069
2. A. Capasso, W. Grattieri, R. Lamedica, and A. Prudenzi A bottom-up approach to residential load modeling IEEE Transactions on Power Systems 9 1994 957 964
3. C.A.C. Coello A comprehensive survey of evolutionary-based multiobjective optimization techniques Knowledge and Information Systems 1 1999 269 308
4. C.A. Coello Coello, and M.S. Lechuga Mopso: A proposal for multiple objective particle swarm optimization Proceedings of the 2002 congress on evolutionary computation, 2002. CEC'02 Vol. 2 2002 IEEE 1051 1056
5. R.C. Eberhart, Y. Shi, and J. Kennedy Swarm intelligence 2001 Elsevier
6. Evora, J., Kremers, E., Morales, S., Hernandez, M., Hernandez, J. J., & Viejo, P. (2011). Agent-based modelling of electrical load at household level. In ECAL 2011: CoSMoS - Proceedings of the 2011 workshop on complex systems modelling and simulation (p. 12).
7. Hagras, H., Packharn, I., Vanderstockt, Y., McNulty, N., Vadher, A., & Doctor, F. (2008). An intelligent agent based approach for energy management in commercial buildings. In IEEE international conference on fuzzy systems, 2008. FUZZ-IEEE 2008. (IEEE world congress on computational intelligence) (pp. 156-162). http://dx.doi.org/10.1109/FUZZY.2008.4630359.
8. Hassan, R., Cohanim, B., De Weck, O., & Venter, G. (2005). A comparison of particle swarm optimization and the genetic algorithm. In Proceedings of the first AIAA multidisciplinary design optimization specialist conference (pp. 18-21).
9. W. Kempton, and J. Tomic Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy Journal of Power Sources 144 2005 280 294 10.1016/j.jpowsour.2004.12.022
10. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings, IEEE international conference on neural networks, 1995 (Vol. 4, pp. 1942-1948). http://dx.doi.org/10.1109/ICNN.1995.488968.
11. J.D. Knowles, and D.W. Corne Approximating the nondominated front using the Pareto archived evolution strategy Evolutionary Computation 8 2000 149 172
12. Kordik, P., Hasaj, M., Tvrdy, M., & Snorek, M. (2009). Building automation simulator and control strategy for intelligent and energy efficient home (pp. 154-159).
13. Lukasiewycz, M., Glaß, M., Reimann, F., & Teich, J. (2011). Opt4J - a modular framework for meta-heuristic optimization. In Proceedings of the genetic and evolutionary computing conference (GECCO 2011) (pp. 1723-1730). Dublin, Ireland.
14. R.T. Marler, and J.S. Arora Survey of multi-objective optimization methods for engineering Structural and Multidisciplinary Optimization 26 2004 369 395
15. G.M. Masters Renewable and efficient electric power systems 2005 John Wiley & Sons
16. D.T. Nguyen, M. Negnevitsky, M. de Groot, and C. Wang Demand response in the retail market: Benefits and challenges Power engineering conference, 2009. AUPEC 2009. Australasian universities 2009 IEEE 1 6
17. Organisation for economic co-operation and development (2011). Green growth studies: Energy.
18. P. Palensky, F. Kupzog, A.A. Zaidi, and K. Zhou Modeling domestic housing loads for demand response Industrial electronics, 2008. IECON 2008. 34th Annual conference of IEEE 2008 2742 2747
19. Parsopoulos, K. E., & Vrahatis, M. N. (2008). Multi-objective particles swarm optimization approaches. Multi-objective optimization in computational intelligence: Theory and practice (pp. 20-42).
20. M. Pedrasa, T.D. Spooner, and I.F. MacGill Scheduling of demand side resources using binary particle swarm optimization IEEE Transactions on Power Systems 24 2009 1173 1181 10.1109/TPWRS.2009.2021219
21. W. Qi, J. Liu, and P.D. Christofides A distributed control framework for smart grid development: Energy/water system optimal operation and electric grid integration Journal of Process Control 21 2011 1504 1516
22. W. Qi, J. Liu, and P.D. Christofides Distributed supervisory predictive control of distributed wind and solar energy systems IEEE Transactions on Control Systems Technology 21 2013 504 512
23. Rajakaruna, S., Shahnia, F., & Ghosh, A. (2015). Plug in electric vehicles in smart grids.
24. M. Reyes-Sierra, and C.C. Coello Multi-objective particle swarm optimizers: A survey of the state-of-the-art International Journal of Computational Intelligence Research 2 2006 287 308
25. Sepulveda, A., Paull, L., Morsi, W. G., Li, H., Diduch, C. P., & Chang, L. (2010). A novel demand side management program using water heaters and particle swarm optimization. In Electric power and energy conference (EPEC), 2010 IEEE (pp. 1-5). http://dx.doi.org/10.1109/EPEC.2010.5697187.
26. M.R. Sierra, and C.A.C. Coello Improving PSO-based multi-objective optimization using crowding, mutation and e-dominance EMO-2005 LNCS Vol. 3410 2005 Springer-Verlag 505 519
27. S.D. Silver, and P. Cowans Small world network model of personal consumption: Demand-side management in an expert system Expert Systems with Applications 35 2008 632 644 10.1016/j.eswa.2007.07.058
28. A. Zhou, B.-Y. Qu, H. Li, S.-Z. Zhao, P.N. Suganthan, and Q. Zhang Multiobjective evolutionary algorithms: A survey of the state of the art Swarm and Evolutionary Computation 1 2011 32 49